The First Nations, Métis, Inuit Indigenous Ontology and Challenges in the Development of an Indigenous Community Vocabulary in the Canadian Context
Bibliographic record
Abstract
Creating and implementing Indigenous-led thesauri and vocabularies for wide adoption by cultural memory institutions is essential to providing respectful terminology to describe materials by and about Indigenous peoples in the territory referred to as Canada. This article details the background, creation, and reflections on the First Nation, Métis, and Inuit, Indigenous Ontology (FNMIIO), up to the release of the first draft in June 2019 as well as more recent initiatives and transformations. Grounded in the recommendations developed by the Canadian Federation of Library Associations’ (CFLA) Truth and Reconciliation Committee, the article discusses the creation of the FNMIIO as an important first step in addressing the need for a widely adoptable, Indigenous run and led thesaurus for use in cultural memory institutions. The article discusses both the methods undertaken in the project and the challenges faced in the development of the FNMIIO and connects the challenges to issues in libraries and the cultural heritage sector in the territory known as Canada as a whole. While a crucial proof-of-concept, the FNMIIO exposed several important issues that must be addressed to fully develop the thesaurus, particularly with respect to ensuring the longevity of the project. While much work remains to make the FNMIIO fully usable by institutions, the initial lessons learned by the CFLA Indigenous Matters Committee’s Joint Working Group as they progressed through the gathering of community names will undergird the next steps for the development and deployment of the FNMIIO.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.027 | 0.030 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".